Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions for coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions. This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications. The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously identify the optimal sequence of tasks it should undertake. The graph weights are determined based on a soft clustering process, which also plays a problem decomposition role seeking to reduce the complexity of the individual-agents' task assignment problems. To evaluate the new Dec-MATA algorithm, a series of case studies (of varying complexity) are performed, with tasks being distributed randomly over an observable 2D environment. A centralized approach, based on a state-of-the-art MILP formulation of the multi-Traveling Salesman problem is used for comparative analysis. While getting within 7-28% of the optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is found to be 1-3 orders of magnitude faster and minimally sensitive to task-to-robot ratios, unlike the centralized algorithm.

More machine learning applications are permeating in the tech ecosystem and the data that goes into ML systems is being derived from all sorts of sources -- regardless of its sensitivity. ML algorithms do not realise the aspect of sensitivity as it always looks at data as a way to establish and learn patterns, rather than looking into the who's who of the data. Miscreants might take advantage of this and circumvent the ML systems itself, which can have devastating effects altogether. If that happens, the purpose of ML will completely fail. To counter this, and establish a secure and safe ML environment, researchers are strictly working towards building privacy in ML models.

This article shows you how we created an xkcd.com We can predict the topic of the comic from the description of the comic. My circle of friends has a huge nerd crush on Randall Munroe, the author of the xkcd comics, and books like what if?. Mary Kate MacPherson took the initiative, and scraped the transcripts for every comic, and used my patent analyzer code to turn the transcripts into embedding vectors. She then tossed the vectors and labels into tensorflow and crunched the data down into clusters using t-SNE. Those of you who know xkcd.com will be well aware that the comics are numbered sequentially. I have a bet going that Randall will mention when the comic number is the same as the current year (2018) and so I have to publish this article quickly!

To kick off a series of Neo4j extensions for machine learning, I implemented a set of user-defined procedures that create a linear regression model in the graph database. In this post, I demonstrate use of linear regression from the Neo4j browser to suggest prices for short term rentals in Austin, Texas. Let's check out the use case: The most popular area in Austin, Texas is identified by the last two digits of its zip code: "04". With the trendiest clubs, restaurants, shops, and parks, "04" is a frequent destination for tourists. Suppose you're an Austin local who's going on vacation.

Some would say it's the magic of a festival - stumbling upon a random stage and accidentally discovering your new favourite band. You could call it following your festival instinct. But what if you ditched all that and did the complete opposite? What if you took arguably the most nerdy thing in the world - statistics - and used it to try to have the best festival experience ever? I consulted a stats expert, packed up a giant bar chart, and headed to 2000 Trees in Gloucestershire to find out. And - just a warning - this article is incredibly, incredibly geeky.

Compared with visible object tracking, thermal infrared (TIR) object tracking can track an arbitrary target in total darkness since it cannot be influenced by illumination variations. However, there are many unwanted attributes that constrain the potentials of TIR tracking, such as the absence of visual color patterns and low resolutions. Recently, structured output support vector machine (SOSVM) and discriminative correlation filter (DCF) have been successfully applied to visible object tracking, respectively. Motivated by these, in this paper, we propose a large margin structured convolution operator (LMSCO) to achieve efficient TIR object tracking. To improve the tracking performance, we employ the spatial regularization and implicit interpolation to obtain continuous deep feature maps, including deep appearance features and deep motion features, of the TIR targets. Finally, a collaborative optimization strategy is exploited to significantly update the operators. Our approach not only inherits the advantage of the strong discriminative capability of SOSVM but also achieves accurate and robust tracking with higher-dimensional features and more dense samples. To the best of our knowledge, we are the first to incorporate the advantages of DCF and SOSVM for TIR object tracking. Comprehensive evaluations on two thermal infrared tracking benchmarks, i.e. VOT-TIR2015 and VOT-TIR2016, clearly demonstrate that our LMSCO tracker achieves impressive results and outperforms most state-of-the-art trackers in terms of accuracy and robustness with sufficient frame rate.

The paper was first presented at TextGraphs-2018, a workshop series at The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) on June 6, 2018 in New Orleans. This new approach to word-sense induction comes from the work of the Lexalytics Magic Machines AI Labs, launched in 2017 in partnership with the University of Massachusetts Amherst's Center for Data Science and Northwestern University's Medill School of Journalism, Media and Integrated Marketing Communications to drive innovation in AI. Word sense induction (WSI) is a challenging task of natural language processing whose goal is to categorize and identify multiple senses of polysemous words from raw text without the help of predefined sense inventory like WordNet (Miller, 1995). The problem is sometimes also called unsupervised word sense disambiguation (Agirre et al., 2006; Pelevina et al., 2016). An effective WSI has wide applications.

Popular libraries make up the backbone of data science: scikit-learn, TensorFlow, Caffe, and Keras are the standard Python choices. But these libraries don't tend to implement niche techniques (scikit-learn's policy actually states that they don't consider algorithms less than three years old or with less than 200 citations!), Enter mlpack: a flexible, fast machine learning library. It's written in C, with bindings to Python and command-line programs that can be used for simpler data science tasks. Because of its use of templates for configurability, it is easy to customize the specific behavior of algorithms without any runtime penalty.

A key aspect behind the success of KAYAK lies in the way we do marketing. Today, our company portfolio consists of 6 brands operating in 60 countries around the world, and successful marketing strategies are vital to ensure further global expansion. To aid our strategic decisions, we apply a range of advanced analytics tools to measure and compare the performance of different marketing activities. One challenging problem in particular is to ensure that we provide a fair comparison between offline (TV) and online marketing (Facebook, YouTube, etc.) for use in high level budget allocation. To resolve this problem, we developed a customized machine learning framework that measures the individual contribution of each of our activities and uses the evaluation to recommend an optimal media mix.

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.